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Record W4404135034 · doi:10.31219/osf.io/rmpzx

Socioemotional Well-Being and Literacy in Syrian Refugee Children: Long-Term Outcomes Post-Resettlement

2024· preprint· en· W4404135034 on OpenAlexaboutno aff
Sara Qadoumi, Angela Capani, Brooke Wortsman, Henry Brice, Raabya Rashad, Mandana Jafarian, Hassan Abdulrasul, Sherry Y. Wu, Kaja Kinga Jasińska

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsSocioemotional selectivity theoryRefugeeTerm (time)LiteracySyrian refugeesPsychologyDevelopmental psychologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Many refugee children face challenges that negatively affect behavioral and academic outcomes post-resettlement. The present study explored socioemotional well-being (SEWB) and language and literacy skills in Syrian refugee children (N=60, Mage=13.63) in the longer-term (1-13 years) after resettlement in Canada. SEWB was assessed using the Strengths and Difficulties questionnaire. Language and literacy skills were assessed with a comprehensive battery of standardized measures. Although most participants exhibited typical SEWB, a significant portion exhibited low SEWB. Participants displayed difficulties in all language and literacy skills. Low SEWB was found to be significantly related to poor word reading, phonological awareness and passage comprehension. Time since resettlement was not related to SEWB or literacy, suggesting literacy difficulties persist post-resettlement. Our findings demonstrate a relation between SEWB and academic outcomes, and show the importance of interventions that bolster not only academics, but the mental health needs of target populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.363
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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